Analysis of influential factors and the construction of a risk prediction model for tigecycline-related drug-induced cholestatic liver disease

LIU Lina; WANG Jianqing; ZHANG Lun; YU Jun

doi:10.6039/j.issn.1001-0408.2025.20.12

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Analysis of influential factors and the construction of a risk prediction model for tigecycline-related drug-induced cholestatic liver disease

更新时间：2025-10-25

- Analysis of influential factors and the construction of a risk prediction model for tigecycline-related drug-induced cholestatic liver disease
- ZHONGGUO YAOFANG Vol. 36, Issue 20, Pages: 2555-2560(2025)
- 作者机构：
  
  安徽省公共卫生临床中心（安徽医科大学第一附属医院北区）药学部，合肥　230012
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2025.20.12
  CLC： R969.3
- Received：04 June 2025，
  
  Revised：2025-09-17，
  
  Accepted：18 September 2025，
  
  Published：30 October 2025
- 稿件说明：
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刘丽娜,王建青,张伦,等.替加环素相关药物性胆汁淤积性肝病的影响因素分析及风险预测模型构建 [J].中国药房,2025,36(20):2555-2560.

LIU Lina,WANG Jianqing,ZHANG Lun,et al.Analysis of influential factors and the construction of a risk prediction model for tigecycline-related drug-induced cholestatic liver disease[J].ZHONGGUO YAOFANG,2025,36(20):2555-2560.
刘丽娜,王建青,张伦,等.替加环素相关药物性胆汁淤积性肝病的影响因素分析及风险预测模型构建 [J].中国药房,2025,36(20):2555-2560. DOI： 10.6039/j.issn.1001-0408.2025.20.12.

LIU Lina,WANG Jianqing,ZHANG Lun,et al.Analysis of influential factors and the construction of a risk prediction model for tigecycline-related drug-induced cholestatic liver disease[J].ZHONGGUO YAOFANG,2025,36(20):2555-2560. DOI： 10.6039/j.issn.1001-0408.2025.20.12.

摘要

目的

分析替加环素（TGC）相关药物性胆汁淤积性肝病（DIC）的影响因素，并建立风险预测模型。

方法

收集2022年8月－2024年8月于我院接受TGC治疗的707例住院患者的临床资料，按8∶2的比例将其随机分为训练集（

＝566）和测试集（

＝141）。通过最小绝对收缩和选择算子回归分析筛选预测变量，采用多因素Logistic回归分析筛选TGC相关DIC的独立危险因素，并基于上述因素绘制列线图预测模型；采用受试者操作特征曲线（ROC曲线）及其曲线下面积（AUC）评估模型的预测性能，采用Hosmer-Lemeshow拟合优度检验和校准曲线评估模型的准确度，采用决策曲线分析评估模型的临床净收益。

结果

707例患者中，93例患者发生DIC，发生率为13.15%。性别、年龄、TGC大剂量给药、入住重症监护室、TGC用药时间、合并使用抗真菌药伏立康唑是TGC相关DIC发生的独立危险因素（

＜0.05）。训练集模型ROC曲线的AUC为0.745（95%置信区间为0.687～0.801），灵敏度为76.6%，特异度为60.3%；测试集模型ROC曲线的AUC为0.762（95%置信区间为0.650～0.900），灵敏度为81.3%，特异度为72.0%。训练集、测试集模型Hosmer-Lemeshow拟合优度检验的

分别为5.187、9.980，

分别为0.737、0.266；校准曲线的平均绝对误差分别为0.012、0.038；风险阈值范围分别为4%～45%、4%～28%。

结论

患者的年龄、性别、TGC大剂量给药、入住重症监护室、TGC用药时间、合并使用抗真菌药伏立康唑是TGC相关DIC发生的独立危险因素；基于以上因素所建的TGC相关DIC预测模型的预测性能、准确度均较好。

Abstract

OBJECTIVE

To analyze the influential factors of drug-induced cholestatic liver disease （DIC） related to tigecycline （TGC）， and establish a prediction model for the risk of this adverse reaction.

METHODS

Data of 707 hospitalized patients

who received TGC treatment in our hospital from August 2022 to August 2024 were collected and randomly divided into training set （

＝566） and test set （

＝141） at a ratio of 8∶2. Prediction variables were screened using the least absolute shrinkage and selection operator regression analysis. Multivariate Logistic regression analysis was used to screen the independent risk factors for TGC-related DIC， and a nomogram prediction model was drawn based on the above factors. The prediction performance of the model was evaluated by the receiver operator characteristic curve （ROC curve） and its area under the curve （AUC）. The accuracy of the model was assessed by the Hosmer-Lemeshow goodness-of-fit test and calibration curves. The clinical net benefit of the prediction model were evaluated by decision curve analysis.

RESULTS

Among the 707 patients， 93 patients developed DIC， with an incidence rate of 13.15%. Gender， age， high-dose administration of TGC， intensive care unit （ICU） admission， duration of medication of TGC， and concurrent use of antifungal drug voriconazole were independent risk factors for the occurrence of TGC-related DIC （

＜0.05）. The AUC of the training set model was 0.745 （95%CI： 0.687-0.801）， with a sensitivity of 76.6% and a specificity of 60.3%. The AUC of ROC curve of the test set model was 0.762 （95%CI： 0.650-0.900）， with a sensitivity of 81.3% and a specificity of 72.0%. The Hosmer-Lemeshow goodness-of-fit test for the training set， the

value was 5.187 and

was 0.737； and for the test set， the

value was 9.980 and

was 0.266. The mean absolute error of the calibration curve for the training set was 0.012， and for the test set， it was 0.038. The risk threshold range for the training set was 4%-45%， and for the test set， it was 4%-28%.

CONCLUSIONS

Age， gender， high-dose administration of TGC， ICU admission， duration of medication of TGC， and concurrent use of antifungal drug voriconazole are independent risk factors for TGC-related DIC. The established TGC-related DIC risk prediction model has good prediction performance and accuracy.

关键词

Keywords

references

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